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Product Content Management

The process of data normalization involves reducing data to its canonical form i.e. standard form of expressing all the data available. Often, especially in software security, malicious input can be entered that could make the whole database vulnerable. This issue can be mitigated with normalized input that ensures that encoding is eliminated and input data is converted into a common character set. One can be totally assured that the online interfaces like Questudio’s Online Catalog Software - WebCat is made free of any sort of malicious activity. The database organization that inherently takes place because of data normalization also allows reduction of redundant data. This becomes vital for services like Product Information Management, omni-channel publishing, product content management etc.

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Product Content Management

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  1. Product Content Management – A solution to manage your data efficiently

  2. Product Content Management - A solution to manage your data efficiently The process of data normalization involves reducing data to its canonical form i.e. standard form of expressing all the data available. Often, especially in software security, malicious input can be entered that could make the whole database vulnerable. This issue can be mitigated with normalized input that ensures that encoding is eliminated and input data is converted into a common character set. One can be totally assured that the online interfaces like Questudio’s Online Catalog Software - WebCat is made free of any sort of malicious activity. The database organization that inherently takes place because of data normalization also allows reduction of redundant data. This becomes vital for services like Product Information Management, omni-channel publishing, product content management etc. More often than not organizations can find themselves entrapped in the data mire and data search becomes an equally monstrous task. Data normalization poses to solve many of such data involving problems.

  3. Data Management • Normalization gives rise to smaller tables with smaller rows, thus accommodating more rows per page. This in turn leads to use of less physical space and cache leading to more efficient systems. Data integrity is yet another advantage of normalized data as there is no redundant and neglected data. This helps in gaining the confidence of the omni-channel customer as same data is reflected across all the channels that he cruises. • 2. Search and Sort • The database tables get narrower after normalization which speeds up searching, sorting and creating indexes. This is crucial for functioning of PIM software dealing with volumes of data and for any sort of search query. Index searching gets faster as index is short also leading to effectual sorting of data. More often than not an omni-channel customer likes to weigh all his options before making his final choice. With paucity of customer browsing time it is logical to provide him with all his alternatives in the shortest possible time. Questudio’somni-channel Publishing makes sure that the customer can make an informed choice in less time.

  4. 3. Physical Space • Physical placement of organized and reduced data becomes more structured. Disk space reduces considerably. This in turn helps in reducing the costs involved in creation and management of disk space. Product Information Management hence becomes a cost effective procedure. • 4. Data Modification • With normalization, better control of data can be achieved as databases becomes more compact. Faster updates in terms of adding, deleting and editing data can be done. Catalog Creation Software implements this principle of allowing quick addition or deletion of products according to the organization’s instantaneous decisions and market conditions. The probability of errors reduce considerably as multiple copies do not exist. • Multiple users can modify data at different instances and at different locations without the fear of incongruent data being entered. Concurrent resolution of data happens automatically with data normalization in place.

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